Knowledge of the moisture content of soil is valuable for hydrology and climate studies, as well as for yield prediction or agricultural planning. As part of the long-term research plan at the Canada Centre for Remote Sensing (CCRS) to establish a relationship between radar backscatter and the spatial and temporal variations of soil moisture, a series of experiments were conducted in the Canadian prairies in 1988.This paper examines radar backscatter as a function of soil moisture, plant type, and phenological development. Airborne data were acquired by the CCRS C-band SAR of a test site near Outlook, Saskatchewan, in June and August 1988. The digitally recorded and processed imagery were externally calibrated via point targets of known radar cross-section. Soil dielectric measurements were collected using a portable dielectric probe in fields with similar surface roughness characteristics. These measurements were used as input to a model developed by CCRS for estimating soil volumetric water content.This paper describes the development of relationships between soil moisture under wheat and canola canopies and radar backscatter. The relationships were developed using the relatively calibrated SAR, the estimate of soil volumetric water content derived from soil dielectric measurements, plant type, and phenological development. The analysis indicated a strong correlation between radar backscatter and volumetric soil moisture under both wheat and canola canopies and that the relationship is dependent on crop type and phenological development.
Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the importance of multisource data and adequate test and train data for wetland classification at a countrywide scale.
Abstract The effect of different tillage on the microwave backscatter from grain stubble plots was investigated as a function of frequency, polarization, row direction and type of tillage implement. The Canada Centre for Remote Sensing (CCRS) truck-mounted FM-CW scatterometer was used to make the radar backscattering coefficient (σ°) measurements. Factorial analysis found the effects of row direction to be as significant as the differences between tillage implements on σ° of like polarized data. The cross-polarized data were less sensitive to row direction effects. There were no significant differences betwen the HH and VV polarizations at any frequency investigated. An airborne SAR image of an area close to the experiment site was qualitatively used to demonstrate the effects of tillage and row direction. The scatterometer results and airborne SAR interpretations indicate the necessity of accounting for row direction, as well as surface roughness, when using SAR data for soil mapping applications. Techniques are identified which may allow estimation of these roughness effects. Radarsat, and other Earth observation satellites will view agricultural fields with many row aspects and thus research needs to be conducted to determine the effects of row direction on σ°at the anticipated azimuth view angles.
The “flood” algorithm code with instruction and details are publicly available at https://github.com/rskelly/flood and as supporting information. The DEM of the PAD is available at https://open.canada.ca/data/en/dataset/03568324-1a8b-4c00-8650-f5c35f6e0bff. Data S1. Supporting Information. Video 1. Broadscale Simulations Video 2. Single Basin Simulation Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.
Polarimetric RADARSAT-2 data of rice and wetlands are used to simulate compact polarimetry (CP) mode data from the upcoming RADARSAT Constellation Mission (RCM). The simulated CP data are then used to evaluate the information content for rice and wetland mapping using supervised classification, and the results are compared for linear and circular polarization combinations and polarimetric decompositions from the fully polarimetric data and the simulated CP data. The results are consistent for both rice and wetlands and show that the classification accuracy increases as one goes up the polarization hierarchy. The circular polarizations produced the best classification results for the polarization combinations. This result requires further research to verify. Although the CP data did not perform as well as the fully polarimetric data, the results were better than for dual polarization, and this mode may offer the best option for rice and wetland mapping applications because of swath coverage. Note that both the compact simulations and the fully polarimetric data produced operationally suitable classification accuracies. Additional research is underway to evaluate the monitoring capability of this new CP mode. This article describes the approach used for the analyses and the classification results for both rice and wetlands.
SUMMARYFour channel synthetic aperture radar imagery was interpreted to determine corn field identification accuracies obtainable using single channel, multi-channel and multi-date radar data. Image tone and texture of agricultural fields in the training set were used to derive discrimination criteria which were applied to fields in the testing set. The confusion of other agricultural crops with corn at X-band frequencies and forest cover with corn at L-band frequencies leads to errors of omission and inclusion. Corn field identification accuracies ranged from 58 to 100% using single channel imagery. The use of multi-channel imagery reduces the identification errors and accuracies exceeding 90% were consistently obtained. Individuals lacking radar interpretation experience but familiar with tonal and textural discrimination on airphotos obtained similar results with a short training period. Only two channels (one X-band, one L-band) are needed for the discrimination of corn from all other surface cover types and by late July the corn should be sufficiently taller than all other crops to enable accurate discrimination. Multi-date imagery is thus not necessary to identify corn fields but may aid in the discrimination of the other agricultural cover types found in the study area.
Large, e.g., provincial or national, scale near-real-time surface water monitoring is an ambitious task, which can be accomplished by using Synthetic Aperture Radar (SAR) satellite data. SAR has demonstrated the ability to distinguish water and land, but there are many common errors of commission and omission that arise due to the side-looking nature of SAR and due to some landcover types with similar backscatter like roads and pasture. A method is proposed to fix/mitigate these errors through the use of combined ascending/descending RADARSAT-2 image pairs and ancillary data. The results of a corrected water/land binary image were, on average, 99.4% accurate for the Boreal Forest Region (Utikuma) of Alberta, Canada, while for the Rocky Mountain Region (Westcastle) also in Alberta, the results proved to be 99.9% accurate when distinguishing water from land. These accuracies were achieved through the reduction of the water false positive rate and a slight reduction in the water true positive rate. These high accuracy values can be partially attributed to the relative low ratios of water to land in the study regions. We hope that these methods can be used and improved in order to move towards large scale dynamic surface water and wetland mapping.
The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.
Wetlands are important natural resources due to their numerous ecological services. Consequently, identifying their locations and extents is imperative. The stability, repeatability, cost-effectiveness, multi-scale coverage, and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications, such as provincial wetland mapping. To do so, it is required to (1) process and classify big geo data (i.e. a large amount of satellite datasets) in a time- and computationally-efficient approach and (2) collect a large amount of field samples. In this study, Google Earth Engine (GEE) and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba, Quebec, and Newfoundland and Labrador (NL). Additionally, using GEE, a generalized supervised classification method is proposed to produce a regional wetland map from a large area (e.g., a province) when lacking field samples. In fact, using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics, the wetland maps were generated for the other two provinces. The overall classification accuracies for Manitoba, Quebec, and NL were 84%, 78%, and 82%, respectively, indicating the high potential of the proposed method for aiding provincial wetland inventory systems.
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